The topic was Intel’s public expression of more than a passing interest in Altera from the perspective of an acquisition. Despite the fact no name could be publicly associated (the following claim is merely attributed to “people familiar with the matter” in the article) with the most important clause in the piece, “Intel Corp. is in advanced talks to buy chip partner Altera Corp”, a lot of editorial content appeared almost instantaneously after the publication of this article in the online WSJ, in what might easily be construed as merely a knee-jerk reaction as the 800 lb gorilla in the PC CPU business starts moving around and sniffing the air.

Is this interest the result of Intel’s obsession with opening other substantial revenue streams? Or is it being prompted by Intel’s inept handling of Altera as its biggest tenant for its foundry business? Or, finally, is it even being prompted by recent market acknowledgement of favorable features of Field Programmable Gateway Architecture (FPGA) semi-conductors (Altera’s main product line) for the development of what amounts to today’s hottest trend in computing — machine learning, algorithms and computer cognition systems. Incidentally, anyone skeptical on this last point should read this call for proposals from the ACM.

I will not take the time here to provide more detail on each of the above points, namely, the need to augment the PC CPU business with something equally compelling for major markets, the foundry business model, or FPGAs as a superior platform for machine learning applications. If you would like further detail on any of these, or all, please contact me and we can talk about it.

If impatient readers with a keen interest in either player in this drama still think it is very important to put together a strategy now to plan for this acquisition taking place, it might save them a lot of effort to simply mention “this notion has come up before” as a quick look at Analyst: Intel may acquire FPGA vendor, which was published back in 2010 will corroborate.

Bottom line, we need further word from Intel and Altera before any one of us should write much more about this. The setting simply is not clear enough, now, to warrant all of this chatter.

Machine learning solutions, and those of the “deep learning” variety are playing an ever increasing role in daily computing activities for most people. This condition does not look to change anytime soon.

But regardless, ISVs with products targeted to the predictive analytics market, or the robotics market, or any one of many emerging new market segments, need to tune in on public perception about these technologies in the mature global markets (US, Western Europe, Japan). Public perception has the potential to prod government regulators towards counter-productive pronouncements. Therefore, it makes sense for ISVs to mount a public relations effort to ensure public perception about these technologies stays “on track”.

On Tuesday, February 24, 2015 the Wall Street Journal published an article germaine to this topic. The piece was written by Timothy Aeppel and is titled What Clever Robots Mean for Jobs. The employment theme is a very familiar one for anyone involved with efforts to use computer processes to automate repetitive tasks. So Aeppel’s skepticism about just whether or not an exploding market of robotics solutions will lead to more jobs, or not (which appears to be his position) is really nothing new.

But the timing of the article, in close proximity to several other articles from “prominent” individuals (Bill Gates, Stephen Hawking and more) about the dangers presented by algorithms should they be applied to computing lends power to Aeppel’s thoughts. Readers should also not lose sight of the 2016 Presidential election here in the States, where ostensible candidates like Hillary Clinton are starting to stake out turf about “hi tech” and its performance as a job creator.

I encourage readers to go back to my first points in this post. Methods of automating processes, including requirements for prediction, are increasing and becoming more accessible to “average” consumers of computing services. This is not a bad thing. On the contrary, in my opinion the accessibility of comparatively powerful methods of enhancing the accuracy of prediction is a net positive contribution to overall business and certainly a likely simulant for new business activity.

Do new businesses create jobs? I am not sure as to the answer to this question, but I can posit they certainly empower more entrepreneurs. Machine learning ISVs and their deep learning siblings need to step forward and do a better job of educating the public about the real benefit of these technologies.

Thanks to Mikio Braun, who on Thursday, January 2015 published an article on the InfoQ website. Braun’s article includes mention of a Google acknowledgement about the role played by machine learning (also known, at least in part, as data processing by algorithms) as a predictive tool in its ad placement technology for its click ad business. Readers interested in this topic should read Braun’s article, which is titled Google on the Technical Debt of Machine Learning.

I have written about the inaccuracy of click advertising in earlier posts to this blog. To quickly summarize my opinion on this topic, I found the systemic tendency towards poor ad placement to be especially difficult to overcome when the items to be promoted provide subjective, intangible benefit. So gaining a perspective on just how much of the ad placement technology behind Google Adwords and, in all likelihood, its direct competitors (principally Microsoft’s Bing advertising system), as Braun points out in this short article is very helpful.

What is also very helpful in Braun’s article is the manner in which the Google researchers (Braun’s article is really a news report on a presentation at a recent conference event held in Montreal, the Software Engineering for Machine Learning workshop, part of the annual Neural Information Processing Systems, NIPS, conference held in Montreal) shed light on the precariousness of proper performance for machine learning systems, in this (online advertising) context, given the effect they have on other related computer processes. These researchers make clear how the basic assumptions powering Neural Networks can actually adversely affect these siblings, and, thereby, produce erroneous results along with very little value to people depending on them. Readers should note this conclusion is my own, and not a conclusion expressed anywhere in Braun’s article.

From Braun’s article, and the technical précis of a research paper on the algorithmic process behind machine learning, which was also published by Google researchers, online advertisers should be careful to set realistic expectations about paid placements. Perhaps it will make sense to horizontally structure these campaigns, with a panoramic reach wherever possible, if they are to produce any meaningful results.